Statistics Toolkit CLI 📊

A comprehensive command-line statistics learning and analysis tool with step-by-step explanations

🎯 Overview

Transform your terminal into a comprehensive statistics education platform! This toolkit provides step-by-step explanations for statistical concepts, making it perfect for students, educators, and anyone who wants to understand the "why" behind statistical calculations.

✨ Features

• 📈 Descriptive Statistics: Mean, median, mode, variance, standard deviation, five-number summary, outlier detection
• 🧪 Hypothesis Testing: One-sample and two-sample t-tests with complete explanations
• 📊 Data Visualization: Histograms, box plots, scatter plots with statistical overlays
• 💾 Data Management: CSV import/export, save/load datasets
• 🎓 Educational Focus: Every calculation shows mathematical steps and reasoning
• 🖥️ Interactive CLI: User-friendly menu-driven interface

🚀 Quick Start

Installation

# Clone the repository
git clone https://github.com/connorodea/statistics-toolkit-cli.git
cd statistics-toolkit-cli

# Install dependencies
pip install -r requirements.txt

# Run the toolkit
python stats_cli.py

One-Line Setup

curl -sSL https://raw.githubusercontent.com/connorodea/statistics-toolkit-cli/main/setup_stats_toolkit.sh | bash

📖 Usage Examples

Interactive Menu

python stats_cli.py

Sample Data Demo

python stats_cli.py --demo

Quick Analysis

Enter values: 85 92 78 88 95 82 79 91 87 84

=== MEASURES OF CENTER ===
Sample size: n = 10
MEAN CALCULATION:
x̄ = Σx/n = 861/10 = 86.1000

MEDIAN CALCULATION:
Sorted data: [78 79 82 84 85 87 88 91 92 95]
n is even: median = (85 + 87)/2 = 86.0000

🎓 Educational Philosophy

This toolkit is designed for learning statistics, not just computing answers. Every calculation shows:

• 📝 The formula being used
• 🔢 Step-by-step substitution of values
• 📊 Intermediate calculations
• 💡 Interpretation of results
• ✅ Assumption checking

📁 Project Structure

statistics-toolkit-cli/
├── stats_cli.py              # Main application
├── requirements.txt          # Python dependencies
├── setup.py                  # Package installation
├── Makefile                  # Development commands
├── examples/
│   ├── data/                 # Sample CSV datasets
│   └── create_sample_data.py # Generate sample data
├── tests/
│   └── test_basic.py         # Unit tests
└── docs/                     # Documentation

🛠️ Development

Setup Development Environment

# Clone and setup
git clone https://github.com/connorodea/statistics-toolkit-cli.git
cd statistics-toolkit-cli

# Create virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Install as editable package
pip install -e .

Available Commands

make run          # Run the CLI tool
make demo         # Run sample data demo
make test         # Run tests
make sample-data  # Generate sample datasets
make clean        # Clean build artifacts
make help         # Show all commands

Running Tests

python -m pytest tests/ -v
# or
make test

📊 Sample Datasets

The toolkit includes sample datasets for practice:

• test_scores.csv - Student academic performance data
• sales_data.csv - Business sales and advertising data
• study_vs_scores.csv - Study time vs exam performance

Generate fresh sample data:

make sample-data

🤝 Contributing

Contributions are welcome! Here's how to get started:

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes
4. Add tests for new functionality
5. Run tests: make test
6. Commit your changes: git commit -m 'Add amazing feature'
7. Push to the branch: git push origin feature/amazing-feature
8. Open a Pull Request

Development Guidelines

• Follow PEP 8 style guidelines
• Add docstrings to new functions
• Include tests for new features
• Update README if adding major features
• Maintain the educational focus of explanations

🐛 Issues & Support

• Bug Reports: Create an issue
• Feature Requests: Create an issue
• Questions: Start a discussion

📚 Documentation

• Installation Guide
• User Manual
• Developer Guide
• API Reference

🏆 Inspiration

Originally inspired by TI-84 calculator programs, this toolkit brings the same step-by-step educational approach to modern Python development, making statistics accessible and understandable for everyone.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built for statistics education and learning
• Inspired by the need for transparent statistical calculations
• Designed to complement traditional statistics textbooks and courses

---

Made with ❤️ for statistics education

📦 PyPI Package Installation

Once published to PyPI, install with:

pip install statistics-toolkit-cli

Then use anywhere:

statstoolkit
# or  
stats-cli

🏗️ Building the Package

# Build for PyPI
make build

# Test locally
python -m statistics_toolkit_cli.cli --demo

# Check package
make package-info

🚀 Publishing to PyPI

# Publish to Test PyPI first
bash scripts/publish_to_pypi.sh  # Choose option 1

# Test installation
pip install --index-url https://test.pypi.org/simple/ statistics-toolkit-cli

# Then publish to real PyPI
bash scripts/publish_to_pypi.sh  # Choose option 2